CIND820 Project Course Code

Ryan Boyd

Libraries imported

Data loaded and prepared

The full original dataset is too large to work with so it is clipped to a smaller study area which was arbitratily selected. The dataframe is queried by the study area longitude and latitude boundary. The pre-classified ground points are removed (since we are not concerned with ground right now), which is class 2, class 1 is the unclassified points and we only want to work with these.

Data normalized and preprocessed for analysis

Add Imagery Data

2015 Imagery data was obtained from the City of Vancouver to extract the RGB values

The image was clipped using external software (QGIS, open-source mapping program) to the same area of interest as above

The selected image size is 4084x4084, the lidar data is normalized by 4084 to extract the nearest pixel value(r,g,b) from the image for each point

The nearest R,G,B pixel from the image is extracted and the coordinates are saved as a field to the data frame to be used in the model

The R,G,B values are normalized like the rest:

Dataset statistics and information if needed

Testing and looking for improvements to be made

Initial Classification (unsupervised) attempt using kmeans clustering

Variables: Height, Intensity, Number of Returns

Visualization

Initial Distance Clustering

Analysis of results

Z/elevation has a large effect on the classifier

R,G,B causes the shadows to be classified

Updates